235 citations · 901 across the 97 of their papers we have counts for
12 papers · 1 filter
Log Barriers for Safe Non-convex Black-box Optimization
Ilnura Usmanova, Andreas Krause, Maryam Kamgarpour
We address the problem of minimizing a smooth function over a compact set defined by smooth functional constraints given noisy value m…
Safe non-smooth black-box optimization with application to policy search
Ilnura Usmanova, Andreas Krause, Maryam Kamgarpour
For safety-critical black-box optimization tasks, observations of the constraints and the objective are often noisy and available only for the feasible points. We propose an approa…
Safe Exploration for Interactive Machine Learning
Matteo Turchetta, Felix Berkenkamp, Andreas Krause
In Interactive Machine Learning (IML), we iteratively make decisions and obtain noisy observations of an unknown function. While IML methods, e.g., Bayesian optimization and active…
Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization
Matteo Turchetta, Andreas Krause, Sebastian Trimpe
In reinforcement learning (RL), an autonomous agent learns to perform complex tasks by maximizing an exogenous reward signal while interacting with its environment. In real-world a…
Adaptive Sampling for Stochastic Risk-Averse Learning
Sebastian Curi, Kfir. Y. Levy, Stefanie Jegelka +1
In high-stakes machine learning applications, it is crucial to not only perform well on average, but also when restricted to difficult examples. To address this, we consider the pr…
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling
Mojmír Mutný, Michał Dereziński, Andreas Krause
We analyze the convergence rate of the randomized Newton-like method introduced by Qu et. al. (2016) for smooth and convex objectives, which uses random coordinate blocks of a Hess…